1. Field of the Invention
The present invention relates to the field of metabolomics, which is the study of small molecules produced by an organism's metabolic processes. More particularly, the embodiments of the present invention are adapted to compile and compare metabolomic data received from a spectrometry device across a plurality of samples. Furthermore, embodiments of the present invention may also provide for the display of a visual indication of the presence of selected metabolites in each of the plurality of samples such that metabolomic data may be subjectively analyzed by a user across the plurality of samples.
2. Description of Related Art
Metabolomics is the study of the small molecules, or metabolites, contained in a cell, tissue or organ (including fluids) and involved in primary and intermediary metabolism. The term “metabolome” refers to the collection of metabolites present in an organism. The human metabolome encompasses native small molecules (natively biosynthesizeable, non-polymeric compounds) that are participants in general metabolic reactions and that are required for the maintenance, growth and normal function of a cell. Thus, metabolomics is a direct observation of the status of cellular physiology, and may thus be predictive of disease in a given organism. Subtle biochemical changes (including the presence of selected metabolites) are inherent in a given disease. Therefore, the accurate mapping of these changes to known pathways may allow researchers to build a biochemical hypothesis for a disease. Based on this hypothesis, the enzymes and proteins critical to the disease can be uncovered such that disease targets may be identified for treatment with targeted pharmaceutical compounds.
Molecular biology techniques for uncovering the biochemical processes underlying disease have been centered on the genome, which consists of the genes that make up DNA, which is transcribed into RNA and then translated to proteins, which then make up the small molecules of the human metabolome. While genomics (study of the DNA-level biochemistry), transcript profiling (study of the RNA-level biochemistry), and proteomics (study of the protein-level biochemistry) are useful for identification of disease pathways, these methods are complicated by the fact that there exist over 25,000 genes, 100,000 to 200,000 RNA transcripts and up to 1,000,000 proteins in human cells. However, it is estimated that there may be as few as 2,500 small molecules in the human metabolome.
Thus, metabolomic technology provides a significant leap beyond genomics, transcript profiling, and/or proteomics. With metabolomics, metabolites, and their role in the metabolism may be readily identified. In this context, the identification of disease targets may be expedited with greater accuracy than with any other known methods. The collection of metabolomic data for use in identifying disease pathways is generally known in the art, as described generally in U.S. patent application Ser. No. 10/757,616, entitled Methods for Drug Discovery, Disease Treatment, and Diagnosis Using Metabolomics. Additional uses for metabolomics data are described therein and include, for example, determining response to a therapeutic agent (i.e., drug) or other xenobiotics, monitoring drug response, determining drug safety, and drug discovery. However, the collection and sorting of metabolomic data taken from a variety of biological samples (e.g., from a patient population) consumes large amounts of time and computational power. For example, according to some known metabolomic techniques, spectrometry data for biological samples is collected and plotted in three dimensions and stored in an individual file corresponding to each biological sample. This data is then individually compared to data corresponding to a plurality of known metabolites in order to identify known metabolites that may be disease targets. The data may also be used for identification of toxic agents and/or drug metabolites. Furthermore such data may also be used to monitor the effects of xenobiotics. However, conventional “file-based” methods (referring to the data file generated for each biological sample) require the use of large amounts of computing power and memory assets to handle the screening of large numbers of known metabolites. Furthermore, “file-based” data handling does not lend itself to the compilation of sample population data across a number of samples because, according to known metabolomic data handling techniques, each sample is analyzed independently, without taking into account subtle changes in metabolite composition that may be more readily detectable across a sample population. Furthermore, existing “file-based” method have other limitations including: limited security and audit ability; poor data set consistency across multiple file copies; and individual files do not support multiple indices (example day collected, sample ID, control vs. treated, drug dose, etc) such that all files must be scanned when only a subset is desired.
These limitations in current metabolomic data analysis techniques may lead to the discarding of potentially relevant and/or valuable metabolomic data that may be used to identify and classify particular metabolites as disease targets. Specifically, spectrometry data corresponding to a number of biological samples (such as tissue samples from individual human subjects) generally results in a large data file corresponding to each biological sample, wherein each data file must then be subjected to a screening process using a library of known metabolites. However, conventional systems do not readily allow for the consolidation of spectrometry data from a number of biological samples for the subjective evaluation of the data generated by the spectrometry processes. Thus, while a single file corresponding to an individual sample may be inconclusive, such data may be more telling if viewed subjectively in a succinct format with respect to other samples within a sample population.
Therefore, there exists a need for an improved system to solve the technical problems outlined above that are associated with conventional metabolomic data analysis systems. More particularly, there exists a need for a system capable of automatically receiving spectrometry data without the need to generate a separate data file for each biological sample. There also exists a need for a system capable of converting three-dimensional data sets into a corresponding two-dimensional data set and plot, that may then be compared to a plurality of characteristic plots corresponding to selected metabolites. In addition, there exists a need for a system for allowing a user to subjectively evaluate the spectrometry data across a plurality of samples to identify selected metabolites.